Financial Time Series Volatility Breakpoint Detection under a Bayesian Framework

This course project builds a Bayesian model for detecting structural changes in financial time series volatility. It derives a joint likelihood with a discrete breakpoint and distinct volatility parameters, then estimates the model with a Random Walk Metropolis-Hastings sampler.

The project compares Uniform, Inverse-Gamma, and Exponential prior settings and applies the method to 2008 S&P 500 index data. The results are used to interpret volatility changes around the Lehman Brothers bankruptcy.